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Research Papers

Residual Elimination Algorithm Enhancements to Improve Foot Motion Tracking During Forward Dynamic Simulations of Gait

[+] Author and Article Information
Jennifer N. Jackson

Department of Biomedical Engineering,
University of Florida,
Gainesville, FL 32611;
Functional and Applied Biomechanics Section,
Rehabilitation Medicine Department,
National Institutes of Health,
Bethesda, MD 20892

Chris J. Hass

Department of Applied Physiology
and Kinesiology,
University of Florida,
Gainesville, FL 32611

Benjamin J. Fregly

Department of Mechanical
and Aerospace Engineering,
University of Florida,
Gainesville, FL 32611;
Department of Biomedical Engineering,
University of Florida,
Gainesville, FL 32611
e-mail: fregly@ufl.edu

1The majority of the research was completed at the University of Florida.

2Corresponding author.

Manuscript received January 23, 2015; final manuscript received August 19, 2015; published online September 16, 2015. Assoc. Editor: Paul Rullkoetter.

J Biomech Eng 137(11), 111002 (Sep 16, 2015) (8 pages) Paper No: BIO-15-1028; doi: 10.1115/1.4031418 History: Received January 23, 2015; Revised August 19, 2015

Patient-specific gait optimizations capable of predicting post-treatment changes in joint motions and loads could improve treatment design for gait-related disorders. To maximize potential clinical utility, such optimizations should utilize full-body three-dimensional patient-specific musculoskeletal models, generate dynamically consistent gait motions that reproduce pretreatment marker measurements closely, and achieve accurate foot motion tracking to permit deformable foot-ground contact modeling. This study enhances an existing residual elimination algorithm (REA) Remy, C. D., and Thelen, D. G., 2009, “Optimal Estimation of Dynamically Consistent Kinematics and Kinetics for Forward Dynamic Simulation of Gait,” ASME J. Biomech. Eng., 131(3), p. 031005) to achieve all three requirements within a single gait optimization framework. We investigated four primary enhancements to the original REA: (1) manual modification of tracked marker weights, (2) automatic modification of tracked joint acceleration curves, (3) automatic modification of algorithm feedback gains, and (4) automatic calibration of model joint and inertial parameter values. We evaluated the enhanced REA using a full-body three-dimensional dynamic skeletal model and movement data collected from a subject who performed four distinct gait patterns: walking, marching, running, and bounding. When all four enhancements were implemented together, the enhanced REA achieved dynamic consistency with lower marker tracking errors for all segments, especially the feet (mean root-mean-square (RMS) errors of 3.1 versus 18.4 mm), compared to the original REA. When the enhancements were implemented separately and in combinations, the most important one was automatic modification of tracked joint acceleration curves, while the least important enhancement was automatic modification of algorithm feedback gains. The enhanced REA provides a framework for future gait optimization studies that seek to predict subject-specific post-treatment gait patterns involving large changes in foot-ground contact patterns made possible through deformable foot-ground contact models.

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Figures

Grahic Jump Location
Fig. 1

Schematics of the 29 DOF one back joint (a) and 29 or 32 DOF two back joint (b) full-body gait models used to evaluate the enhanced REA. The static markers for both the right and left legs are the medial and lateral knee, the medial and lateral ankle, and the medial and lateral toe joint markers. Dark blue circles indicate static markers removed after the static trial and light red circles indicate dynamic markers.

Grahic Jump Location
Fig. 2

Schematic of our enhanced REA (modified from [13]). Experimental marker coordinates c′ and ground reactions F′ are input to the algorithm. The algorithm adjusts model initial conditions q0 and q˙0, assumed experimental joint kinematics q′, q˙′, q¨′, model inertial and joint parameter values m, I, p, and optimal feedback gains kp and kv. Generalized accelerations q¨ calculated by residual elimination are numerically integrated to determine corresponding generalized speeds q˙ and coordinates q. An inverse kinematics analysis is then used to calculate corresponding model marker coordinates c, which are compared with experimental marker coordinates at the same time frame.

Grahic Jump Location
Fig. 4

Enhanced REA RMS marker distance errors over 5 trials for each segment for all locomotion tasks: walking, marching, running, and bounding. Error bars indicate one standard deviation away from the mean. For the marching motion, only four useable trials were available. Leg errors include the pelvis, thigh, shank, and foot. Units are in mm.

Grahic Jump Location
Fig. 3

Comparison between experimental markers (dark blue circles) and model markers (light red dots) for walking, marching, running, and bounding at 0%, 25%, 50%, 75%, and 100% of the locomotion cycle. Units are in meters.

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